The Topography of Striatal Dopamine and Symptoms in Psychosis: An Integrative PET and MRI study

Background: Striatal dopamine dysfunction is thought to underlie symptoms in psychosis, yet it remains unclear how a single neurotransmitter could cause the diverse presentations that are observed clinically. One hypothesis is that the consequences of aberrant dopamine signalling vary depending on where within the striatum the dysfunction occurs. Positron emission tomography (PET) allows for the quantification of dopamine function across the striatum. In the current study we use a novel method to investigate the relationship between spatial variability in dopamine synthesis capacity and psychotic symptoms. Methods: We used a multimodal imaging approach combining 18F-DOPA PET and resting state MRI in 29 patients with first episode psychosis and 21 healthy controls. In each participant, resting state functional connectivity maps were used to quantify the functional connectivity of each striatal voxel to well-established cortical networks. Network-specific striatal dopamine synthesis capacity(Kicer) was then calculated for the resulting connectivity defined parcellations. Results: The connectivity defined parcellations generated Kicer values with equivalent reliability, and significantly greater orthogonality to standard anatomical parcellation methods. As a result, dopamine-symptom associations were significantly different from one another for different subdivisions, whereas no unique subdivision relationships were found when using an anatomical parcellation. In particular, dopamine function within striatal areas connected to the default mode network was strongly associated with negative symptoms(p<0.001). Conclusion: These findings suggest that individual differences in the topography of dopamine dysfunction within the striatum contribute to shaping psychotic symptomatology. Further validation of the novel approach in future studies is necessary

Mesolimbic Dopamine Function Is Related to Salience Network Connectivity: An Integrative Positron Emission Tomography and Magnetic Resonance Study

BACKGROUND: A wide range of neuropsychiatric disorders, from schizophrenia to drug addiction, involve abnormalities in both the mesolimbic dopamine system and the cortical salience network. Both systems play a key role in the detection of behaviorally relevant environmental stimuli. Although anatomical overlap exists, the functional relationship between these systems remains unknown. Preclinical research has suggested that the firing of mesolimbic dopamine neurons may activate nodes of the salience network, but in vivo human research is required given the species-specific nature of this network. METHODS: We employed positron emission tomography to measure both dopamine release capacity (using the D2/3 receptor ligand 11C-PHNO, n = 23) and dopamine synthesis capacity (using 18F-DOPA, n = 21) within the ventral striatum. Resting-state functional magnetic resonance imaging was also undertaken in the same individuals to investigate salience network functional connectivity. A graph theoretical approach was used to characterize the relationship between dopamine measures and network connectivity. RESULTS: Dopamine synthesis capacity was associated with greater salience network connectivity, and this relationship was particularly apparent for brain regions that act as information-processing hubs. In contrast, dopamine release capacity was associated with weaker salience network connectivity. There was no relationship between dopamine measures and visual and sensorimotor networks, indicating specificity of the findings. CONCLUSIONS: Our findings demonstrate a close relationship between the salience network and mesolimbic dopamine system, and they are relevant to neuropsychiatric illnesses in which aberrant functioning of both systems has been observed

Prisoner's Dilemma in Cancer Metabolism

As tumors outgrow their blood supply and become oxygen deprived, they switch to less energetically efficient but oxygen-independent anaerobic glucose metabolism. However, cancer cells maintain glycolytic phenotype even in the areas of ample oxygen supply (Warburg effect). It has been hypothesized that the competitive advantage that glycolytic cells get over aerobic cells is achieved through secretion of lactic acid, which is a by-product of glycolysis. It creates acidic microenvironment around the tumor that can be toxic to normal somatic cells. This interaction can be seen as a prisoner's dilemma: from the point of view of metabolic payoffs, it is better for cells to cooperate and become better competitors but neither cell has an incentive to unilaterally change its metabolic strategy. In this paper a novel mathematical technique, which allows reducing an otherwise infinitely dimensional system to low dimensionality, is used to demonstrate that changing the environment can take the cells out of this equilibrium and that it is cooperation that can in fact lead to the cell population committing evolutionary suicide

Fibroblasts—a key host cell type in tumor initiation, progression, and metastasis

Tumor initiation, growth, invasion, and metastasis occur as a consequence of a complex interplay between the host environment and cancer cells. Fibroblasts are now recognized as a key host cell type involved in host–cancer signaling. This review discusses some recent studies that highlight the roles of fibroblasts in tumor initiation, early progression, invasion, and metastasis. Some clinical studies describing the prognostic significance of fibroblast-derived markers and signatures are also discussed

Comprehensive Analysis of Affymetrix Exon Arrays Using BioConductor

ISSN:1553-734XISSN:1553-735

Incidence and Risk Factors of Serious Adverse Events during Antituberculous Treatment in Rwanda: A Prospective Cohort Study

BACKGROUND: Tuberculosis (TB) and TB-human immunodeficiency virus infection (HIV) coinfection is a major public health concern in resource-limited settings. Although TB treatment is challenging in HIV-infected patients because of treatment interactions, immunopathological reactions, and concurrent infections, few prospective studies have addressed this in sub-Saharan Africa. In this study we aimed to determine incidence, causes of, and risk factors for serious adverse events among patients on first-line antituberculous treatment, as well as its impact on antituberculous treatment outcome. METHODS AND FINDINGS: Prospective observational cohort study of adults treated for TB at the Internal Medicine department of the Kigali University Hospital from May 2008 through August 2009. Of 263 patients enrolled, 253 were retained for analysis: median age 35 (Interquartile range, IQR 28-40), 55% male, 66% HIV-positive with a median CD4 count 104 cells/mm(3) (IQR 44-248 cells/mm(3)). Forty percent had pulmonary TB, 43% extrapulmonary TB and 17% a mixed form. Sixty-four (26%) developed a serious adverse event; 58/167 (35%) HIV-infected vs. 6/86 (7%) HIV-uninfected individuals. Commonest events were concurrent infection (n = 32), drug-induced hepatitis (n = 24) and paradoxical reactions/TB-IRIS (n = 23). HIV-infection (adjusted Hazard Ratio, aHR 3.4, 95% Confidence Interval, CI 1.4-8.7) and extrapulmonary TB (aHR 2, 95%CI 1.1-3.7) were associated with an increased risk of serious adverse events. For TB/HIV co-infected patients, extrapulmonary TB (aHR 2.0, 95%CI 1.1-3.9) and CD4 count <100 cells/mm3 at TB diagnosis (aHR 1.7, 95%CI 1.0-2.9) were independent predictors. Adverse events were associated with an almost two-fold higher risk of unsuccessful treatment outcome at 6 months (HR 1.89, 95%CI 1.3-3.0). CONCLUSION: Adverse events frequently complicate the course of antituberculous treatment and worsen treatment outcome, particularly in patients with extrapulmonary TB and advanced immunodeficiency. Concurrent infection accounts for most events. Our data suggest that deterioration in a patient already receiving antituberculous treatment should prompt an aggressive search for additional infections

Variation in Cooperative Behaviour within a Single City

Human cooperative behaviour, as assayed by decisions in experimental economic dilemmas such as the Dictator Game, is variable across human populations. Within-population variation has been less well studied, especially within industrial societies. Moreover, little is known about the extent to which community-level variation in Dictator Game behaviour relates to community-level variation in real-world social behaviour. We chose two neighbourhoods of the city of Newcastle upon Tyne that were similar in most regards, but at opposite ends of the spectrum in terms of level of socioeconomic deprivation. We administered Dictator Games to randomly-selected residents, and also gathered a large number of more naturalistic measures of cooperativeness. There were dramatic differences in Dictator Game behaviour between the two neighbourhoods, with the mean allocation to the other player close to half the stake in the affluent neighbourhood, and close to one tenth of the stake in the deprived neighbourhood. Moreover, the deprived neighbourhood was also characterised by lower self-reported social capital, higher frequencies of crime and antisocial behaviour, a higher frequency of littering, and less willingness to take part in a survey or return a lost letter. On the other hand, there were no differences between the neighbourhoods in terms of the probability of helping a person who dropped an object, needed directions to a hospital, or needed to make change for a coin, and people on the streets were less likely to be alone in the deprived neighbourhood than the affluent one. We conclude that there can be dramatic local differences in cooperative behaviour within the same city, and that these need further theoretical explanation

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power